Abstract
We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB). It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms.
Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 25 |
Subtitle of host publication | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
Pages | 2249-2257 |
Number of pages | 9 |
Volume | 3 |
State | Published - Dec 1 2012 |
Event | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: Dec 3 2012 → Dec 6 2012 |
Other
Other | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
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Country/Territory | United States |
City | Lake Tahoe, NV |
Period | 12/3/12 → 12/6/12 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Signal Processing